
where \(p_h(y|\mathbf {z}_{a})\)  is the probability distribution after taking softmax of semantic projection classifier \(h\) , output logits. Weighting the \(D_2\)  output with the class probabilities helps in achieving stable training [1]}.
Finally, we update the visual and semantic encoders (\(f\)  and \(g\) ) to minimize discrepancy between the embeddings (\(\mathbf {z}_{a}\)  and \(\mathbf {z}_{v}\) ) in the latent space, given by:
